For more details
📝 Paper: bmva-archive.org.uk/bmvc/2025/a...
💻 Code: github.com/valeoai/muddos

This is a joint work with my great co-authors @alexandreboulch.bsky.social, @gillespuy.bsky.social, @tuanhungvu.bsky.social, Renaud Marlet, @ncourty.bsky.social and myself.

Key findings:
1️⃣ The LiDAR backbone architecture has a major impact on cross-domain generalization.
2️⃣ A single pretrained backbone can generalize to many domain shifts.
3️⃣ Freezing the pretrained backbone + training only a small MLP head gives the best results.

We systematically study how to best exploit vision foundation models (like DINOv2) for UDA on LiDAR data and identify practical “recipes” that consistently give strong performance across challenging real-world domain gaps.

and Aniruddha Kembhavi, Adrien Gaidon, Nicolas Mansard, and Justin Carpentier as afternoon ones

Thank you @skamalas.bsky.social ! Looking Forward to my Journey in Grenoble !

The visualisation of the shifts is really great! Although finishing a thesis on domain adaptation for 3D, these shifts in the formal definition always remain a bit abstract for me, whereas with the visualisation in the space(s) it is much clearer.

I really enjoyed it! Generating the dataset myself, made it very easy to start and play with. Also while knowing on a high level the ideas of flow matching, it was great to do it once myself and to see also the steps in the code.

Looks great ! I am sure some of your colleagues in the lab would also be interested to have a look in a lunch break on these handhelds 😅